stella54443099

Lalaki
Mga Social Link
Siya ay
Physiology, Glucocorticoids StatPearls NCBI Bookshelf

**Account**
The "Account" section is typically used to identify the user or organization that owns this document. In many public‑domain or institutional repositories, no specific account information is required, so you can leave it blank or note that the content is provided under a public‑domain license.

---

## Overview

This guide explains how to create a **single‑file PDF** that contains all of your web pages (the original HTML files, their CSS stylesheets, JavaScript assets, images, fonts, etc.). The result is a stand‑alone document that can be opened on any computer without requiring an Internet connection or a web server.

---

## Step 1 – Gather All Files

1. **Copy the entire project folder** (the root of your website).
2. Verify that the folder contains:
- `index.html` and other HTML pages.
- Sub‑folders: `css/`, `js/`, `images/`, `fonts/`, etc.
3. If you use a build tool (Webpack, Gulp, etc.), run its *build* command so that all assets are in their final form.

---

## Step 2 – Create an HTML "Index" Page

This page will serve as the starting point for printing:

```html

Print Demo

Demo Page

This is a test of printing.

```

**Tip:** Keep the CSS minimal or inline to avoid external dependencies.

---

## 3. Printing and Scanning

### 3.1 How to Print
- **Print**: `Ctrl + P` (Windows) / `Cmd + P` (Mac).
- Choose a printer, set pages, and click "Print".

### 3.2 How to Scan
- **Scan**: Use the scanner’s software or Windows’ "Scanner" app.
- Save scanned images as PNG/JPG.

---

## 4. Converting Images to Text

### 4.1 OCR (Optical Character Recognition)
Use an OCR tool like Google Cloud Vision, Tesseract, or Adobe Acrobat:
```bash
tesseract image.png output_text -l eng
```
- `image.png`: scanned PNG file
- `output_text.txt`: extracted text

---

## 5. Working with the Data

### 5.1 Extracting a Dictionary from JSON
```python
import json
with open('data.json', 'r') as f:
data = json.load(f)

# Assuming keys are strings like "0", "1", etc.
dict_data = int(k): v for k, v in data.items()
```

### 5.2 Converting JSON to YAML
```python
import yaml
with open('data.json', 'r') as f:
json_dict = json.load(f)

yaml_string = yaml.dump(json_dict, sort_keys=False)
print(yaml_string)
```

### 5.3 Pretty Printing a Dictionary
```python
from pprint import pprint

pprint(dict_data, width=1) # Each key-value pair on its own line
```

---

## 4. Common Pitfalls & Tips

| Scenario | What to Avoid | Recommendation |
|----------|---------------|----------------|
| **Large JSON files** | Loading entire file into memory (`json.load`) | Use `ijson` for streaming or read in chunks if the structure allows |
| **Indentation errors** when writing YAML | Mixing tabs and spaces | Configure your editor to use spaces only (usually 2–4 per level) |
| **Nested dicts with many levels** | Manual pretty‑printing becomes hard | Use `pprint` or a dedicated library (`yamale`, `ruamel.yaml`) that handles indentation automatically |
| **Non‑serializable objects** (e.g., datetime) | Directly dumping to JSON/YAML raises TypeError | Convert them to strings first (`isoformat()`) or write custom serializer functions |
| **Large files** | Memory usage spikes when loading entire file | Process line by line with streaming parsers (e.g., `jsonlines`, `ruamel.yaml` with `Loader=SafeLoader`) |

---

## 4. Summary of Key Take‑aways

| What to Do | Why It Matters |
|------------|----------------|
| **Convert data types before serialization** | JSON/YAML expect primitive types; complex objects break the process. |
| **Use safe loaders/dumpers** | Avoid code injection or accidental execution (e.g., `yaml.safe_load`). |
| **Handle large files with streaming** | Prevents memory exhaustion and improves performance. |
| **Validate schema/structure** | Ensures downstream systems can reliably consume data. |
| **Keep serialization consistent across environments** | Avoid platform‑specific quirks that could corrupt the file. |

By keeping these principles in mind, developers can smoothly transition data from Python objects into robust JSON or YAML files suitable for configuration, logging, or inter‑service communication.

---

## 4. Final Checklist

- Convert your Python object to a serializable structure (list/dict).
- Use `json.dump` or `yaml.safe_dump` with appropriate options.
- Verify the file is valid and readable by your target consumers.
- Handle errors (e.g., I/O, encoding) gracefully.

Happy coding!
https://quickdate.click/@emiliourner268

Mag-browse ng Musika

Ang iyong musika

stella54443099

Mga tagasunod

Mga Artist na Susundan

Mga Lingguhang Nangungunang Track

Nakapila